Original Research ARTICLE
Prediction in Autism by Deep Learning Short-time Spontaneous Hemodynamic Fluctuations
- 1Shanghai Institute of Advanced Communications and Data Science, China
- 2School of Computer Engineering and Science, Shanghai University, China
- 3Shanghai University, China
- 4Centre for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University, China
- 5Key Lab for Behavioral Economic Science & Technology, South China Normal University, China
This study aims to explore the possibility of using a multilayer artificial neural network for the classification between children with autism spectrum disorder (ASD) and typically developing (TD) children based on short-time spontaneous hemodynamic fluctuations. Spontaneous hemodynamic fluctuations were collected by a functional near-infrared spectroscopy setup from bilateral inferior frontal gyrus and temporal cortex in 25 children with ASD and 22 TD children. To perform feature extraction and classification, a multilayer neural network called CGRNN was used which combined a convolution neural network (CNN) and a gate recurrent unit (GRU), since CGRNN has a strong ability in finding characteristic features and acquiring intrinsic relationship in time series. For the training and predicting, short-time (7 s) time-series raw functional near-infrared spectroscopy (fNIRS) signals were used as the input of the network. To avoid the over-fitting problem and effectively extract useful differentiation features from a sample with a very limited size (e.g., 25 ASDs and 22 TDs), a sliding window approach was utilized in which the initially recorded long-time (e.g., 480 s) time-series was divided into many partially overlapped short-time (7 s) sequences. By using this combined deep-learning network, a high accurate classification between ASD and TD could be achieved even with a single optical channel, e.g., 92.2% accuracy, 85.0% sensitivity, and 99.4% specificity. This result implies that the multilayer neural network CGRNN can identify characteristic features associated with ASD even in a short-time spontaneous hemodynamic fluctuation from a single optical channel, and second, the CGRNN can provide highly accurate prediction in ASD.
Keywords: ASD, fNIRS, Neural Network, time series, CGRNN model
Received: 19 Jun 2019;
Accepted: 03 Oct 2019.
Copyright: © 2019 Xu, Geng, He, Li and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Prof. Jun Li, Centre for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China, firstname.lastname@example.org
Prof. Jie Yu, School of Computer Engineering and Science, Shanghai University, Shanghai, Shanghai, China, email@example.com